Sources of false positives in computer-assisted CT colonography

CT colonography with computer-aided detection: recognizing the causes of false-positive reader results

Computed tomography (CT) colonography is a screening modality used to detect colonic polyps before they progress to colorectal cancer. Computer-aided detection (CAD) is designed to decrease errors of detection by finding and displaying polyp candidates for evaluation by the reader. CT colonography CAD false-positive results are common and have numerous causes. The relative frequency of CAD false-positive results and their effect on reader performance on the basis of a 19-reader, 100-case trial shows that the vast majority of CAD false-positive results were dismissed by readers. Many CAD false-positive results are easily disregarded, including those that result from coarse mucosa, reconstruction, peristalsis, motion, streak artifacts, diverticulum, rectal tubes, and lipomas. CAD false-positive results caused by haustral folds, extracolonic candidates, diminutive lesions (<6 mm), anal papillae, internal hemorrhoids, varices, extrinsic compression, and flexural pseudotumors are almost always recognized and disregarded. The ileocecal valve and tagged stool are common sources of CAD false-positive results associated with reader false-positive results. Nondismissable CAD soft-tissue polyp candidates larger than 6 mm are another common cause of reader false-positive results that may lead to further evaluation with follow-up CT colonography or optical colonoscopy. Strategies for correctly evaluating CAD polyp candidates are important to avoid pitfalls from common sources of CAD false-positive results.

Evaluation of computer-aided detection and diagnosis systems

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer-aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer-aided diagnosis systems provide an assessment of a disease using image-based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and "best practices" for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end-users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in clinical practice.

Volumetric detection of colorectal lesions for noncathartic dual-energy computed tomographic colonography

Noncathartic computed tomographic colonography (CTC) could significantly increase patient adherence to colorectal screening guidelines. However, radiologists find the interpretation of noncathartic CTC images challenging. We developed a fully automated computer-aided detection (CAD) scheme for assisting radiologists with noncathartic CTC. A volumetric method is used to detect lesions within a thick target region encompassing the colonic wall. Dual-energy CTC (DE-CTC) is used to provide more detailed information about the colon than what is possible with conventional CTC. False-positive detections are reduced by use of a random-forest classifier. The effect of the thickness of the target region on detection performance was assessed by use of 22 clinical noncathartic DE-CTC studies including 27 lesions ≥6 mm. The results indicate that the thickness parameter can have significant effect on detection accuracy. Leave-one-patient-out evaluation indicated that the proposed CAD scheme detects colorectal lesions at high accuracy in noncathartic CTC.

CAD-associated reader error in CT colonography

RATIONALE AND OBJECTIVES

Computed tomographic colonographic interpretation with computer-aided detection (CAD) may be superior to unaided viewing, although polyp characteristics may influence accuracy. Reader error due to polyp characteristics was evaluated in a multiple-case, multiple-reader trial of computed tomographic colonography with CAD.

MATERIALS AND METHODS

Two experts retrospectively reviewed 52 positive cases (74 polyps) and categorized them as hard, moderate, or easy to detect. Each case was evaluated without and with CAD. Features that may influence a reader's ability to detect a polyp or to accept or reject a CAD mark were tabulated. The association between polyp characteristics and detection rates in the trial was assessed. The difference in detection rates (CAD vs unassisted) was calculated, and regression analysis was performed.

RESULTS

Of 64 polyps found by CAD, experts categorized 20 as hard, 28 as moderate, and 16 as easy to detect. Reader characterization errors predominated (47.3%) over other errors. Factors associated with lower detection rates included small size, flat morphology, and resemblance to a thickened fold. CAD was superior for polyps resembling lipomas compared to those that did not resemble lipomas (average increase in detection rate with CAD, 12.8% vs 5.5%; P < .05).

CONCLUSIONS

Polyp characteristic may impair computed tomographic colonographic interpretation augmented by CAD. Readers can avoid errors of measurement by evaluating diminutive polyp candidates with sample measurements. Caution should be taken when evaluating focally thick folds and when using visual impression to dismiss a polyp candidate as a lipoma when it is submerged in densely tagged fluid.

Computer-aided detection of colorectal polyps in CT colonography with and without fecal tagging: a stand-alone evaluation

PURPOSE

To evaluate the stand-alone performance of a computer-aided detection (CAD) algorithm for colorectal polyps in a large heterogeneous CT colonography (CTC) database that included both tagged and untagged datasets.

METHODS

Written, informed consent was waived for this institutional review board-approved, HIPAA-compliant retrospective study. CTC datasets from 2063 patients were assigned to training (n = 374) and testing (n = 1689). The test set consisted of 836 untagged and 853 tagged examinations not used for CAD training. Examinations were performed at 15 sites in the United States, Asia, and Europe, using 4- to 64-multidetector-row computed tomography and various acquisition parameters. CAD sensitivities were calculated on a per-patient and per-polyp basis for polyps measuring ≥6 mm. The reference standard was colonoscopy in 1588 (94%) and consensus interpretation by expert radiologists in 101 (6%) patients. Statistical testing employed χ, logistic regression, and Mann-Whitney U tests.

RESULTS

In 383 of 1689 individuals, 564 polyps measuring ≥6 mm were identified by the reference standard (347 polyps: 6-9 mm and 217 polyps: ≥10 mm). Overall, CAD per-patient sensitivity was 89.6% (343/383), with 89.0% (187/210) for untagged and 90.2% (156/173) for tagged datasets (P = 0.72). Overall, per-polyp sensitivity was 86.9% (490/564), with 84.4% (270/320) for untagged and 90.2% (220/244) for tagged examinations (P = 068). The mean false-positive rate per patient was 5.14 (median, 4) in untagged and 4.67 (median, 4) in tagged patient datasets (P = 0.353).

CONCLUSION

Stand-alone CAD can be applied to both tagged and untagged CTC studies without significant performance differences. Detection rates are comparable to human readers at a relatively low false-positive rate, making CAD a useful tool in clinical practice.